Interactive voice response using intent prediction and, for example a 5g capable device

ABSTRACT

Disclosed here is a method to determine a user intent when a user device initiates an interactive voice response (IVR) call with a wireless telecommunication network. A processor can detect the IVR call initiated with the network and determine whether the user device is a member of the network. Upon determining that the user device is a member of the network, the processor can obtain user history including interaction history between the user and the network. Based on the user history, the processor can predict the user intent when the user initiates the IVR call. The processor can detect whether user device is a 5G capable device. Upon the determining that the device is 5G capable and based on the predicted user intent, the processor can suggest to the user an application configured to execute on the user device and configured to address the predicted user intent.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.17/525,681, filed on Nov. 12, 2021, and entitled INTERACTIVE VOICERESPONSE USING INTENT PREDICTION AND, FOR EXAMPLE A 5G CAPABLE DEVICE,which is a continuation of U.S. patent application Ser. No. 17/009,673,filed on Sep. 1, 2020, and entitled INTERACTIVE VOICE RESPONSE USINGINTENT PREDICTION AND A 5G CAPABLE DEVICE, the disclosure of which arehereby incorporated herein in their entireties by reference.

BACKGROUND

Interactive voice response (IVR) allows users to interact with acompany's host system via a telephone keypad or by speech recognition,after which services can be inquired about through the IVR dialogue.Usually, the IVR systems can respond with pre-recorded audio containingstatic menus to further direct users on how to proceed. The static menuscan be complex, and after a few selections, the user can forget all theselections made, and feel lost in the labyrinth of options.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a logical architecture of an IVR system capable of intentprediction.

FIG. 2 is a diagram of an example of an IVR workflow, according to thepresent technology.

FIG. 3 is a diagram of a workflow using the prior history of interactionbetween the user and the IVR system to predict a user's intent.

FIG. 4 is a diagram of a workflow to predict user intent and suggestadditional actions to a user, once a user states the intent to the IVRsystem.

FIG. 5 is a flowchart of a method to determine a user intent when a userdevice initiates an interactive voice response (IVR) call with awireless telecommunication network.

FIG. 6 is a diagrammatic representation of a machine in the example formof a computer system within which a set of instructions, for causing themachine to perform any one or more of the methodologies or modulesdiscussed herein, can be executed.

DETAILED DESCRIPTION

Disclosed here is a method to determine a user intent when a userinitiates an IVR call with a service provider for a wirelesstelecommunication network. A processor can detect the IVR call initiatedwith the service provider for the wireless telecommunication network anddetermine whether the user is a member of, or subscriber with, thewireless telecommunication network. Upon determining that the user is amember of the wireless telecommunication network, the processor canobtain history associated with the user including interaction historybetween the user and the wireless telecommunication network or a userbilling status associated with the user of the wirelesstelecommunication network. Based on the history associated with theuser, the processor can predict the user intent when the user initiatesthe IVR call. Upon determining that the user is a member of the wirelesstelecommunication network, the processor can detect whether a deviceused to initiate the IVR call is a 5G capable device. Upon thedetermining that the device is the 5G capable device and based on thepredicted user intent, the processor can suggest to the user anapplication configured to execute on the device used to initiate the IVRcall and configured to address the predicted user intent.

Even if the user device is not a 5G capable device, the processor canimprove the IVR call experience by predicting the user intent, asdescribed herein. Upon initiating the IVR call, the processor can verifythe predicted user intent by receiving a natural language input from theuser verifying the predicted user intent. Based on the predicted userintent, the processor can engage a software application, such as a bot,configured to address the predicted user intent. If the predicted userintent is not accurate, upon attempting to verify the user intent, theprocessor can receive a natural language input from the user indicatingthat the predicted user intent is not accurate and stating the actualintent. The processor can then determine the accurate user intent fromthe natural language input and engage a second software configured toaddress the accurate user intent.

Various examples of the invention will now be described. The followingdescription provides certain specific details for a thoroughunderstanding and enabling description of these examples. One skilled inthe relevant technology will understand, however, that the invention canbe practiced without many of these details. Likewise, one skilled in therelevant technology will also understand that the invention can includemany other obvious features not described in detail herein.Additionally, some well-known structures or functions may not be shownor described in detail below, to avoid unnecessarily obscuring therelevant descriptions of the various examples.

FIG. 1 shows a logical architecture of an IVR system capable of intentprediction. The intent prediction IVR system 100 can work with bothtraditional mobile devices 110 such as 4G, 3G, 2G mobile devices, and 5Gmobile devices 120.

Intent prediction IVR system 100 can provide a dynamic menu suited tothe user of the mobile device 110, 120, instead of a static menu,independent of the user's intent and the user's expectation. To predictthe user intent, the prediction module 130 can obtain data from varioussources such as third-party sources 140 including social media posts,billing 150, and/or user's e-commerce activity 190. To obtaininformation about the user associated with the device 110, 120, thesystem 100 can determine a unique identifier of the device 110, 120,such as the phone number. Based on that unique identifier, the system100 accesses various subsystems 140, 150, 160, 170, 180, 190 to gatherdata about the user.

If the device unique identifier is not associated with the varioussubsystems 150, 160, 170, 180, 190, the system 100 can infer that thedevice is not a user of the system 100. Consequently, the system 100 candirect the user to a prospective user line.

For example, user profile 160 stores information about how the userprefers to communicate with the system 100, such as through the web orapplications installed on the device 110, 120, whether the user prefersa voice or video call, etc. User relationship management (CRM) 170 canstore information about previous interactions with the user, such aslocations from which the user called, frequently placed calls, familymembers and relationship between them within the same account profilewho were calling an agent on similar topic. Topic includes billing orhandset issues, product and services inquiry, etc. Enterprise resourceplanning (ERP) 180 can store information about previous user orders,forward and backward logistics of shipping type and status, status ofthe order in the supply chain, etc. E-commerce 190 stores informationabout items for sale, pricing and product bundle, popular items, ameasure of popularity such as number of units sold, which items weresold to the user, etc. Billing 150 stores information about a userbilling cycle, previous and current user bills, and payment type andhistory, e.g. if the user is current or in collection which can lead topotential offers.

A machine learning model 105 can take in the data from various sources140, 150, 160, 170, 180, 190 and determine user behavioral patterns. Forexample, machine learning model 105 can determine the current time, suchas whether it is the beginning or end of the year, or beginning or endof the month. Based on billing patterns stored in billing 150, themachine learning model 105 can determine whether the user tends to makepayments at a particular time of year or month, such as at the beginningof the month, the beginning of the year, at the end of the month, and/orat the end of the year. If the current time overlaps a time when theuser makes a payment, the system 100 can immediately create a dynamicmenu asking the user “are you making a payment?”

In another example, the machine learning model 105 can take data fromthe CRM 170, and based on the user location, determine whether the userfrequently travels internationally, or based on the frequently placedcalls determine whether the user frequently calls internationally. Ifthe user is an international traveler and/or a frequent internationalcaller, the system 100 can offer upgrades to calling plans that offerdiscounts on calls from or to international locations.

In a third example, the system 100 can gather information from socialmedia 140 to determine the user's intent. For example, the user couldhave been posting about a particular device, such as an iPhone, and thesystem 100 can infer that the user intent is to purchase the particulardevice when the user calls the system. In response, the system 100 canpresent the dynamic menu with the question “are you interested inpurchasing an iPhone?”

In a fourth example, the subsystems 150, 160, 170, 180, 190 can alsocollect device 110, 120 diagnostics, such as the hardware specificationsof the device and/or the software specifications. For example, theinformation that the device 110, 120 could have recently downloaded anew operating system upgrade can be stored in one of the subsystems 150,160, 170, 180, 190. When the user places a call to the IVR system 100,the system can create a dynamic menu and ask the user if the user iscalling about the recent software upgrade. Additionally, the devicediagnostics such as processor speed or memory usage can be stored in thesubsystems 150, 160, 170, 180, 190. As a result, the machine learningmodel 105 can detect that there has been a hardware slow down, such asslower processor speed. When the user places a call to the IVR system100, the system can create a dynamic menu and ask the user if the useris calling about the hardware slow down.

In the fifth example, the IVR platform 175 can detect customer datausage based on data store in the subsystems 150, 160, 170 to determineif the user has regularly maxed out allocated internet data that werecausing slowness in device internet usages. Based on the number ofshared lines within the same account and/or the issue of device Internetusage, the system can create a dynamic menu and ask the user if the userwants to switch to a different plan.

In the sixth example, the IVR platform 175 can also detect if a user iscalling into IVR because the user is not able to use internet or notable to place phone call at all through IVR application because thevoice call is not available. With subsystem 150, 160, 170, 180, 190 dataand network backhaul data availability, IVR 175 can query thesesubsystems to determine whether there are wireless telecommunicationnetwork outages at certain location and alert the user before the userreaches out to a live agent.

In response to the query about the intent, the user can provide anatural language response. The system 100 can receive a natural languageresponse by data (e.g. via a text chat session) or voice (i.e., a spokenvoice call). Utilizing natural language processing 115, the system 100can understand the user's response. If the user's response indicatesthat the intent is incorrectly predicted, the system 100 can makeanother prediction about the intent or connect the user to an agent.

If the user indicates that the intent is correctly predicted, a bot 125can guide the user through the step-by-step instructions of realizingtheir intent. For example, if the user is making a one-time payment, thebot 125 can ask the user for payment information and/or the amount ofthe payment. Once the information is received, the bot 125 can completethe payment.

The system 100 can detect whether the device 110, 120 is a 5G handset,or and whether the device 110, 120 is a 4G, or a 3G phone having highbandwidth, such as Wi-Fi. If the device is a 5G handset, the system 100can check whether the 5G handset has access to a 5G wirelesstelecommunication network. If the device is a 5G handset and/or hasaccess to high bandwidth, the system can notify the device 110, 120 thatthere is an application 135 that can address the user's concern. Theapplication 135 can enable the user to navigate menus through theapplication and make all the necessary selections through theapplication. The application may or may not be installed on the device110, 120. For example, the application can be downloaded from cloudstores such as Play Store or Apple Store, or the application can executeremotely on a cloud computer.

Interacting with the application 135 can be preferable to interactingwith the IVR system 100 because the application 135 can provide avisualization of the user's selections performed in the application, asopposed to the system 100 which is an audio system and cannot provide avisualization of user's responses.

In addition, the application 135 can provide faster interaction asopposed to an IVR system using audio-only prompts. The application 135can provide increased accuracy by allowing callers to see multipleoptions before making a selection. The application 135 can provideimproved dialogue with an information-rich, web-based input that allowsalpha-numeric data to be easily entered. The application 135 can improveinteraction experience for a user with a disability such as withhearing, voice and speech challenges. The application 135 can tailorvisual experiences based on what we know about device 110, 120, such aswhether the device is constantly having slowness issues or whether thereis an upcoming bill due date. Based on the user needs and the predicteduser intent, the application 135 can show a dynamic menu instead ofgeneric static menu that does not match each user's needs. The machinelearning model 105 can determine the menu to show in the application 135based on the data gathered from sources 140, 150, 160, 170, 180, 190.

In addition to the application 135, the user device 110, 120 with highthroughput can enable face-to-face interaction with an agent such asthrough video conferencing, real time chats, screen sharing, co-browsinga website, and exchanging image documents and signatures in real time.The agent can be a human agent, or a bot designed to interact with theuser. If the bot is interacting through videoconferencing, aphotorealistic rendering or animation of a person can be presented tothe user. The agent can share the screen, and/or a visualization of thetopic of the conversation between the user and the agent, because theuser device 110, 120 has high throughput such as a 5G wirelesstelecommunication network or a Wi-Fi connection. The agent can pushvisual content onto the user device 110, 120 so that both sides can seethe same content. The visual content can include webpages, videos,images, 3D geometry, etc. By contrast, today's IVR systems can only pushtext messages such as short message service (SMS), but not visualcontent.

The module 145 can perform speech recognition and can include two bots155, 165 which can receive speech through voice or a data plan of thewireless telecommunication network. The module 145 can be Avaya Aura®Experience Portal. The IVR platform 175 can include multiple bots 105,125 that can perform various functions, such as fulfilling purchaseorders, enabling the user to make payments, guiding the user to realizetheir intent, etc.

FIG. 2 is a diagram of an IVR workflow, according to the presenttechnology. The user device 200 can make a call to the IVR system 100 inFIG. 1 by dialing a predesignated number, such as 611, which can connectthe user device 200 to user care.

In step 210, an application can intercept the call and, in step 220,detect whether the device 200 is a 5G device. If the device 200 is not a5G device, in step 230, the system 100 can route the call to atraditional IVR system 240. In step 225, the application can detectwhether the device 200 is on a 5G network. If the device is not on the5G network, even though the device is 5G, the system 100 can route thecall to the traditional IVR system 240.

If the device 200 is a 5G device, then the system 100, in step 250, candetect the intent of the call, based on information gathered fromvarious sources 140, 150, 160, 170, 180, 190 in FIG. 1 . Step 250 caninclude machine learning model 105 in FIG. 1 . For example, the machinelearning model 105 can detect that the user wants to make a paymentbecause the system has detected that the user is late on a payment. Instep 260, the system 100 can display an application 135 in FIG. 1 toenable the user to make a payment on the device 200. The user cannavigate the application in steps 262, 264, 266. If the user is done, instep 280, the system 100 can close the application 135. After making thepayment, if the user still wants to talk to an agent, in step 270, thesystem 100 can connect the user to talk to the agent through a videocall because the device is a 5G device with high bandwidth.

FIG. 3 is a diagram of a workflow using the prior history of interactionbetween the user and the IVR system to predict a user's intent. Thediagram in FIG. 3 presents two calls 300 and 310.

The call 300, occurring on a first day, may not have a relevant priorhistory for several reasons, such as, the user associated with thedevice 320 has not called the IVR system 100 in FIG. 1 before or theprevious call is outside of a predetermined time window so that theprevious call is not considered relevant. The predetermined time windowcan be a week, a month, a year. To determine the client intent duringthe call 300, the system 100, in step 330, can detect the device 320Automatic Number Identification (ANI) and/or Dialed NumberIdentification Service (DNIS). ANI is a feature of a wirelesstelecommunication network for automatically determining the originationtelephone number on toll calls. DNIS is a service that identifies theoriginally dialed telephone number of an inbound call.

In step 340, 345, the system 100 can gather information about the userfrom various sources 140, 150, 160, 170, 180, 190 in FIG. 1 , includingthe user profile and the billing system. In step 350, the system 100 candetect the user name, and predict the user intent, as described in thisapplication. For example, the user intent can be to make a late payment.In step 360, the system 100 can create a voice message, which iscommunicated in step 370, where the IVR system 100 asks the user, byname, if the user wants to make a payment. Once the user accepts, instep 380, the system 100 can lead the user through the payment flow, andin step 390 in the call.

In step 305, the system 100 can store the reason for the call and theresolution in a database.

Next time the user makes a call 310, the system 100, in step 315, candetect whether the user has called earlier within a predetermined timewindow, and can retrieve the reason and the resolution of the previouscall 300. In step 325, the system 100 can remind the user of theprevious call by, for example, thanking the user for making the payment.In addition, the system 100 can ask the user what the intent of the call310 is, or the system 100 can make the prediction of the user intentbased on the reason or the resolution of the previous call 300.

For example, if during the call 300 the user has purchased an item, thesystem 100 can determine that the intent of the user is to get an updateon the status of the purchase. The system 100 can ask the user whetherthe intent is to get the status of the purchase. If that is intent ofthe user, the system 100 can provide the status. If that is not intentof the user, in step 335, the user can state the intent, such as, “Iwould like to speak to an agent.” In step 355, the system can interpretthe user intent from the natural language statement and can realize theintent of the user, by, for example, connecting the user to the agent.

FIG. 4 is a diagram of a workflow to predict user intent and suggestadditional actions to a user, once a user states the intent to the IVRsystem 100 in FIG. 1 . The user device 400 can place a call to the IVRsystem 100. The IVR system 100 can predict the user intent, as describedin this application, or the IVR system can ask the user about his intentin step 410. In step 415, the IVR system 100 can obtain from the userthe natural language input describing the intent.

In step 420, a machine learning model can receive from voice to texttranslation ASR system 165 in FIG. 1 a text version of the naturallanguage provided by the user. The machine learning model, in step 420,can predict a topic of the user's intent, such as billing, purchasing,shipment information, etc.

In step 430, the system 100 can obtain the topic predicted by themachine learning model in step 420 and can gather additional informationassociated with the user from various sources 140, 150, 160, 170, 180,190 in FIG. 1 . In step 440, the system 100 can predict the user intentbased on the stated intent, and in addition can make a suggestion for anadditional action.

For example, in step 440, the system can determine that based on accountbalance and data usage during the last 6 months, the user payment is toohigh. Consequently, the system 100 can recommend upgrading the serviceplan to a different service plan, or upgrading the user device 400 to adifferent user device.

In step 450, the system 100 can create a voice message to the user, forexample, a message including the account balance, payment suggestion,product upgrade offer, network outage alert and/or a suggested action.In step 460, the system 100 can speak to the user and present thesuggestion. In step 470, the user can accept the suggestion. In step480, the system 100 can perform the suggested action, such as performingthe product upgrades. In step 490, the system 400 can end the call.

FIG. 5 is a flowchart of a method to determine a user intent when a userdevice initiates an interactive voice response (IVR) call with awireless telecommunication network.

In step 500, a hardware or software processor executing the instructionsdescribed in this application can detect a communication initiatedbetween a user device and a wireless telecommunication network. Thecommunication can be an IVR call from the user to the customer carecenter of the wireless telecommunication network.

In step 510, the processor can determine whether the user device is amember of the wireless telecommunication network or is otherwiseauthorized to access the network. To determine whether the user deviceis a member, the processor can obtain a unique identifier of the userdevice, such as the phone number, international mobile subscriberidentity (IMSI), etc. and based on the number can determine whether theuser device is member of the network. Alternatively, the processor candetermine the number that the user device called, and if the user devicecalled a specially designated number for customer care, such as 611,likely to be known to user devices that are members, the processor candetermine that the user device is a member of the telecommunicationnetwork. Further, in step 510 the system can determine that the user isauthorized, e.g. by asking the user to provide a PIN code or otherwiseprovide some user-authenticating data to the wireless telecommunicationnetwork.

In step 520, upon determining that the user device is a member of, orsubscriber to, the wireless telecommunication network, the processor canobtain history associated with a user of the user device includinginteraction history between the user and the wireless telecommunicationnetwork or a user billing status associated with the user of thewireless telecommunication network. The history associated with the usercan include user posts on social media, history of user purchases withinthe telecommunication network or outside of the telecommunicationnetwork, shipment status, etc.

In step 530, based on the history associated with the user, theprocessor can predict the user intent when the user initiates thecommunication, before the user provides an input to the processor eitherthrough the user interface or verbally.

In step 540, upon initiating the communication, the processor can verifythe predicted user intent, by for example asking the user “would youlike to purchase an iPhone,” or “would you like to make a payment on theoutstanding balance”.

In step 550, the processor can receive a first natural language inputfrom the user verifying the predicted user intent. For example, the usercan say “yes” to confirm the predicted intent or “no” to indicate thepredicted intent is incorrect.

Based on the first natural language input, the processor can engage asoftware configured to address the user intent. For example, if the userreplied “yes” in step 550, the processor can activate an application onthe user device that is configured to address the user intent, or theprocessor can activate a box configured to interact with the user usingnatural language. In another example, if the user replied “no” in step550, the processor can inquire further what the user intent is.

In addition, the processor can detect whether a device used to initiatethe IVR call is a 5G capable device or if the user device has access toa high bandwidth connection, such as Wi-Fi. Upon the determining thatthe device is 5G capable and/or that the user device has access to thehigh bandwidth connection, and based on the predicted user intent, theprocessor can suggest to the user an application configured to executeon the user device, where the application is configured to address thepredicted user intent. For example, if the user intent is to make apayment, the processor can suggest to the user an application that canrun on the user device and help the user in making the payment. Theapplication can already be installed on the user device, can bedownloaded from a store, or can run on a cloud computer.

In addition, upon the determining that the device is 5G capable and/orthat the user device has access to the high bandwidth connection, theprocessor can push visual content from the wireless telecommunicationnetwork to the device. The visual content can include videos, images,webpages, three-dimensional models, etc. By contrast, the current IVRservice allows pushing of text messages, such as SMS messages, only.

The processor can receive a second natural language input from the userindicating that the predicted user intent is not accurate and indicatingan accurate user intent. The processor can determine the accurate userintent from the second natural language input and can engage a secondsoftware configured to address the accurate user intent. For example,the processor can suggest to the user a second application configured toexecute on the user device, where the second application is configuredto address the accurate user intent. The processor can also initiate thecommunication between the user and an agent associated with the wirelesstelecommunication network. The communication can include creating avideo conference session between the user and an agent associated withthe wireless telecommunication network.

To create the video conference, the processor can detect bandwidthavailable to the user device by detecting whether the device is a 5Gcapable device or connected to a wireless network. Upon the determiningthat the device is the 5G capable device or connected to the wirelessnetwork, the processor can suggest to an agent to a connection type tooffer to the user device. Upon the user device accepting the offer, theagent can create a video conference session between the user and theagent associated with the wireless telecommunication network.

In one example, to predict the user intent, the processor can obtain thebilling status associated with the user. The billing status can includea time of month during which the user tends to make a payment to thewireless telecommunication network. The processor can determine acurrent time of month. The processor can compare the current time ofmonth and the time of month during which the user tends to make thepayment. Upon determining that the current time of month and the time ofmonth during which the user tends to make the payment are substantiallysimilar, the processor can predict that the user intent is to make thepayment to the wireless telecommunication network.

In another example, to predict the user intent, the processor can obtaina device diagnostic associated with the device. The device diagnosticcan include a processor load of the device, memory consumption, networkload, etc. The processor can determine that the device diagnosticexceeds a predetermined threshold, such as by determining that theprocessor load exceeds a predetermined threshold. Upon determining thatthe device diagnostic exceeds the predetermined threshold, such as theprocessor threshold, the processor can predict that the user intent isassociated with the performance of the device.

Computer

FIG. 6 is a diagrammatic representation of a machine in the example formof a computer system 600 within which a set of instructions, for causingthe machine to perform any one or more of the methodologies or modulesdiscussed herein, can be executed.

In the example of FIG. 6 , the computer system 600 includes a processor,memory, non-volatile memory, and an interface device. Various commoncomponents (e.g., cache memory) are omitted for illustrative simplicity.The computer system 600 is intended to illustrate a hardware device onwhich any of the components described in the example of FIGS. 1-5 (andany other components described in this specification) can beimplemented. The computer system 600 can be of any applicable known orconvenient type. The components of the computer system 600 can becoupled together via a bus or through some other known or convenientdevice.

The processor of the computer system 600 can perform the various methodsdescribed in this application, such as methods described in FIGS. 2-5 .The processor of the computer system 600 can be associated with the userdevice 110, 120 in FIG. 1 , and/or the IVR system 100 in FIG. 1 , suchas the IVR platform 175 in FIG. 1 . The user device 110, 120 cancommunicate with the IVR system 100 using the network of the computersystem 600 including a Wi-Fi network, an air interface, and/or a wirednetwork. The main memory, the non-volatile memory, and/or a hard diskdrive unit can store the instructions described in this application,such as instructions described in FIGS. 2-5 .

This disclosure contemplates the computer system 600 taking any suitablephysical form. As example and not by way of limitation, computer system600 can be an embedded computer system, a system-on-chip (SOC), asingle-board computer system (SBC) (such as, for example, acomputer-on-module (COM) or system-on-module (SOM)), a desktop computersystem, a laptop or notebook computer system, an interactive kiosk, amainframe, a mesh of computer systems, a mobile telephone, a personaldigital assistant (PDA), a server, or a combination of two or more ofthese. Where appropriate, computer system 600 can include one or morecomputer systems 600; be unitary or distributed; span multiplelocations; span multiple machines; or reside in a cloud, which caninclude one or more cloud components in one or more networks. Whereappropriate, one or more computer systems 600 can perform withoutsubstantial spatial or temporal limitation one or more steps of one ormore methods described or illustrated herein. As an example and not byway of limitation, one or more computer systems 600 can perform in realtime or in batch mode one or more steps of one or more methods describedor illustrated herein. One or more computer systems 600 can perform atdifferent times or at different locations one or more steps of one ormore methods described or illustrated herein, where appropriate softwareis typically stored in the non-volatile memory and/or the drive unit.Indeed, storing an entire large program in memory may not even bepossible. Nevertheless, it should be understood that for software torun, if necessary, it is moved to a computer-readable locationappropriate for processing, and for illustrative purposes, that locationis referred to as the memory in this application. Even when software ismoved to the memory for execution, the processor will typically make useof hardware registers to store values associated with the software, anda local cache that, ideally, serves to speed up execution. As usedherein, a software program is assumed to be stored at any known orconvenient location (from non-volatile storage to hardware registers)when the software program is referred to as “implemented in acomputer-readable medium.” A processor is considered to be “configuredto execute a program” when at least one value associated with theprogram is stored in a register readable by the processor.

Unless specifically stated otherwise as apparent from the followingdiscussion, it is appreciated that throughout the description,discussions utilizing terms such as “processing” or “computing” or“calculating” or “determining” or “displaying” or “generating” or thelike, refer to the action and processes of a computer system, or similarelectronic computing device, that manipulates and transforms datarepresented as physical (electronic) quantities within the computersystem's registers and memories into other data similarly represented asphysical quantities within the computer system memories or registers orother such information storage, transmission or display devices.

The algorithms and displays presented herein are not inherently relatedto any particular computer or other apparatus. Various general purposesystems can be used with programs in accordance with the teachingsherein, or it can prove convenient to construct more specializedapparatus to perform the methods of some embodiments. The requiredstructure for a variety of these systems will appear from thedescription below. In addition, the techniques are not described withreference to any particular programming language, and variousembodiments can thus be implemented using a variety of programminglanguages.

While the computer-readable medium or computer-readable storage mediumis shown in an exemplary embodiment to be a single medium, the term“computer-readable medium” and “computer-readable storage medium” shouldbe taken to include a single medium or multiple media (e.g., acentralized or distributed database, and/or associated caches andservers) that store the one or more sets of instructions. The term“computer-readable medium” and “computer-readable storage medium” shallalso be taken to include any medium that is capable of storing, encodingor carrying a set of instructions for execution by the machine and thatcause the machine to perform any one or more of the methodologies ormodules of the presently disclosed technique and innovation.

In some circumstances, operation of a memory device, such as a change instate from a binary one to a binary zero or vice versa, for example, cancomprise a transformation, such as a physical transformation. Withparticular types of memory devices, such a physical transformation cancomprise a physical transformation of an article to a different state orthing. For example, but without limitation, for some types of memorydevices, a change in state can involve an accumulation and storage ofcharge or a release of stored charge. Likewise, in other memory devices,a change of state can comprise a physical change or transformation inmagnetic orientation or a physical change or transformation in molecularstructure, such as from crystalline to amorphous or vice versa. Theforegoing is not intended to be an exhaustive list in which a change instate for a binary one to a binary zero or vice versa in a memory devicecan comprise a transformation, such as a physical transformation.Rather, the foregoing are intended as illustrative examples.

A storage medium typically can be non-transitory or comprise anon-transitory device. In this context, a non-transitory storage mediumcan include a device that is tangible, meaning that the device has aconcrete physical form, although the device can change its physicalstate. Thus, for example, non-transitory refers to a device remainingtangible despite this change in state.

Remarks

Unless the context clearly requires otherwise, throughout thedescription and the claims, the words “comprise,” “comprising,” and thelike are to be construed in an inclusive sense, as opposed to anexclusive or exhaustive sense; that is to say, in the sense of“including, but not limited to.” As used herein, the terms “connected,”“coupled,” or any variant thereof means any connection or coupling,either direct or indirect, between two or more elements; the coupling orconnection between the elements can be physical, logical, or acombination thereof. Additionally, the words “herein,” “above,” “below,”and words of similar import, when used in this application, refer tothis application as a whole and not to any particular portions of thisapplication. Where the context permits, words in the above DetailedDescription using the singular or plural number may also include theplural or singular number respectively. The word “or” in reference to alist of two or more items covers all of the following interpretations ofthe word: any of the items in the list, all of the items in the list,and any combination of the items in the list.

The above Detailed Description of examples of the invention is notintended to be exhaustive or to limit the invention to the precise formdisclosed above. While specific examples for the invention are describedabove for illustrative purposes, various equivalent modifications arepossible within the scope of the invention, as those skilled in therelevant art will recognize. For example, while processes or blocks arepresented in a given order, alternative implementations may performroutines having steps, or employ systems having blocks, in a differentorder, and some processes or blocks may be deleted, moved, added,subdivided, combined, and/or modified to provide alternative orsub-combinations. Each of these processes or blocks may be implementedin a variety of different ways. Also, while processes or blocks are attimes shown as being performed in series, these processes or blocks mayinstead be performed or implemented in parallel, or may be performed atdifferent times. Further any specific numbers noted herein are onlyexamples: alternative implementations may employ differing values orranges.

The teachings of the invention provided herein can be applied to othersystems, not necessarily the system described above. The elements andacts of the various examples described above can be combined to providefurther implementations of the invention. Some alternativeimplementations of the invention may include not only additionalelements to those implementations noted above, but also may includefewer elements.

Any patents and applications and other references noted above, and anythat may be listed in accompanying filing papers, are incorporatedherein by reference in the entirety, except for any subject matterdisclaimers or disavowals, and except to the extent that theincorporated material is inconsistent with the express disclosureherein, in which case the language in this disclosure controls. Aspectsof the invention can be modified to employ the systems, functions, andconcepts of the various references described above to provide yetfurther implementations of the invention.

These and other changes can be made to the invention in light of theabove Detailed Description. While the above description describescertain examples of the invention, and describes the best modecontemplated, no matter how detailed the above appears in text, theinvention can be practiced in many ways. Details of the system may varyconsiderably in its specific implementation, while still beingencompassed by the invention disclosed herein. As noted above,particular terminology used when describing certain features or aspectsof the invention should not be taken to imply that the terminology isbeing redefined herein to be restricted to any specific characteristics,features, or aspects of the invention with which that terminology isassociated. In general, the terms used in the following claims shouldnot be construed to limit the invention to the specific examplesdisclosed in the specification, unless the above Detailed Descriptionsection explicitly defines such terms. Accordingly, the actual scope ofthe invention encompasses not only the disclosed examples, but also allequivalent ways of practicing or implementing the invention under theclaims.

To reduce the number of claims, certain aspects of the invention arepresented below in certain claim forms, but the applicant contemplatesthe various aspects of the invention in any number of claim forms. Forexample, while only one aspect of the invention is recited as ameans-plus-function claim under 35 U.S.C. § 112(f), other aspects maylikewise be embodied as a means-plus-function claim, or in other forms,such as being embodied in a computer-readable medium. (Any claimsintended to be treated under 35 U.S.C. § 112(f) will begin with thewords “means for”, but use of the term “for” in any other context is notintended to invoke treatment under 35 U.S.C. § 112(f).) Accordingly, theapplicant reserves the right to pursue additional claims after filingthis application to pursue such additional claim forms, in either thisapplication or in a continuing application.

I/We claim:
 1. An apparatus for use with a wireless telecommunication network, the apparatus comprising: one or more processors coupled to the wireless telecommunication network; and, at least one memory coupled to the one or more processors, wherein the memory includes instructions executable by the one or more processors to: detect a communication initiated between a UE and the wireless telecommunication network; obtain a device diagnostic associated with the UE; based on the device diagnostic, predict an anticipated action when the UE initiates the communication, wherein the anticipated action is associated with a performance of the UE; upon initiating the communication, verify the anticipated action; receive a first natural language input from the UE verifying the anticipated action; and based on the first natural language input, engage a software configured to automatically address the anticipated action.
 2. The apparatus of claim 1, the instructions to predict the anticipated action further comprising the instructions to: obtain a predetermined threshold associated with the device diagnostic; determine whether the device diagnostic exceeds the predetermined threshold; and upon determining that device diagnostic exceeds the predetermined threshold, predict that the anticipated action is associated with the performance of the UE.
 3. The apparatus of claim 1, the instructions further comprising instructions to: receive a second natural language input from the UE indicating that the anticipated action is not accurate and indicating an accurate anticipated action; determine the accurate anticipated action from the second natural language input; and engage a second software configured to address the accurate anticipated action.
 4. The apparatus of claim 1, the instructions further comprising instructions to: receive natural language input from the UE indicating that the anticipated action is not accurate and indicating an accurate anticipated action; and initiate the communication between the UE and an agent associated with the wireless telecommunication network.
 5. The apparatus of claim 1, the instructions further comprising instructions to: detect bandwidth available to the UE by detecting whether the UE is a 5G capable device or connected to a wireless network; and upon determining that the UE is the 5G capable device or connected to the wireless network, create a video conference session between the UE and an agent associated with the wireless telecommunication network.
 6. The apparatus of claim 1, the instructions further comprising instructions to: detect bandwidth available to the UE by detecting whether the UE is a 5G capable device or connected to a wireless network; and upon determining that the UE is the 5G capable device or connected to the wireless network, and based on the anticipated action, suggest to the UE an application configured to execute on the UE, wherein the application is configured to address the anticipated action.
 7. The apparatus of claim 1, the instructions further comprising instructions to: detect bandwidth available to the UE by detecting whether the UE is a 5G capable device or connected to a wireless network; and upon determining that the UE is the 5G capable device or connected to the wireless network, push a visual content from the wireless telecommunication network to the UE.
 8. The apparatus of claim 1, wherein the device diagnostic comprises processor load associated with the UE, memory consumption associated with the UE, or network load associated with the UE.
 9. A method comprising: detecting a communication initiated between a UE and a wireless telecommunication network; obtaining a device diagnostic associated with the UE; based on the device diagnostic, predicting an anticipated action when the UE initiates the communication, wherein the anticipated action is associated with a performance of the UE; upon initiating the communication, verifying the anticipated action; receiving a first natural language input from the UE verifying the anticipated action; and based on the first natural language input, engaging a software configured to automatically address the anticipated action.
 10. The method of claim 9, wherein predicting the anticipated action further comprises: obtaining a predetermined threshold associated with the device diagnostic; determining whether the device diagnostic exceeds the predetermined threshold; and upon determining that device diagnostic exceeds the predetermined threshold, predicting that the anticipated action is associated with the performance of the UE.
 11. The method of claim 9, comprising: receiving a second natural language input from the UE indicating that the anticipated action is not accurate and indicating an accurate anticipated action; determining the accurate anticipated action from the second natural language input; and engaging a second software configured to address the accurate anticipated action.
 12. The method of claim 9, comprising: detecting bandwidth available to the UE by detecting whether the UE is a 5G capable device or connected to a wireless network; and upon determining that the UE is the 5G capable device or connected to the wireless network, and based on the anticipated action, suggesting to the UE an application configured to execute on the UE, wherein the application is configured to address the anticipated action.
 13. The method of claim 9, comprising: detecting bandwidth available to the UE by detecting whether the UE is a 5G capable device or connected to a wireless network; and upon determining that the UE is the 5G capable device or connected to the wireless network, sending a visual content from the wireless telecommunication network to the UE.
 14. At least one non-transitory computer-readable storage medium storing instructions, which, when executed by at least one data processor of a system, cause the system to: detect a communication initiated between a UE and a wireless telecommunication network; obtain a device diagnostic associated with the UE; based on the device diagnostic, predict an anticipated action when the UE initiates the communication, wherein the anticipated action is associated with a performance of the UE; upon initiating the communication, verify the anticipated action; receive a first natural language input from the UE verifying the anticipated action; and based on the first natural language input, engage a software configured to automatically address the anticipated action.
 15. The at least one non-transitory computer-readable storage medium of claim 14, wherein the instructions to predict the anticipated action further comprise the instructions to: obtain a predetermined threshold associated with the device diagnostic; determine whether the device diagnostic exceeds the predetermined threshold; and upon determining that device diagnostic exceeds the predetermined threshold, predict that the anticipated action is associated with the performance of the UE.
 16. The at least one non-transitory computer-readable storage medium of claim 14, the instructions further comprising instructions to: receive a second natural language input from the UE indicating that the anticipated action is not accurate and indicating an accurate anticipated action; determine the accurate anticipated action from the second natural language input; and engage a second software configured to address the accurate anticipated action.
 17. The at least one non-transitory computer-readable storage medium of claim 14, the instructions further comprising instructions to: receive natural language input from the UE indicating that the anticipated action is not accurate and indicating an accurate anticipated action; and initiate the communication between the UE and an agent associated with the wireless telecommunication network.
 18. The at least one non-transitory computer-readable storage medium of claim 14, the instructions further comprising instructions to: detect bandwidth available to the UE by detecting whether the UE is a 5G capable device or connected to a wireless network; and upon determining that the UE is the 5G capable device or connected to the wireless network, create a video conference session between the UE and an agent associated with the wireless telecommunication network.
 19. The at least one non-transitory computer-readable storage medium of claim 14, the instructions further comprising instructions to: detect bandwidth available to the UE by detecting whether the UE is a 5G capable device or connected to a wireless network; and upon determining that the UE is the 5G capable device or connected to the wireless network, and based on the anticipated action, suggest to the UE an application configured to execute on the UE, wherein the application is configured to address the anticipated action.
 20. The at least one non-transitory computer-readable storage medium of claim 14, the instructions further comprising instructions to: detect bandwidth available to the UE by detecting whether the UE is a 5G capable device or connected to a wireless network; and upon determining that the UE is the 5G capable device or connected to the wireless network, push a visual content from the wireless telecommunication network to the UE. 